Acoustic cardiac assessment

ABSTRACT

A non-invasive method and apparatus for monitoring of the function of the heart and lungs in vulnerable patients. An analysis of the activity of the heart is made in correspondence to the respiratory system. Using the method of the invention, precise tracking of the changes of the mutual heart-lung interactions cycle are made, enabling better definitions of heart conditions. Within breath variability factor is introduced for tracking heart condition. Failing heart assisting methods and improved diagnostic methods are facilitated using the monitoring system of the invention.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to non-invasive cardiac and respiratorymonitors. More specifically, diagnostic tools and methods for evaluatingthe heart and respiratory system and improving the function of thefailing heart are disclosed.

BACKGROUND OF THE INVENTION

Cardiopulmonary monitoring of clinically unstable patients is essentialfor early detection of potentially life threatening changes in thefunctionality of the heart or respiration. Cardiopulmonary monitoring ispresently done on hospitalized patients in critical care, such asintensive care units (ICU), coronary care units (CCU) andperi-operatively as well as in other specialized sections of thehospital. Certain types of cardiopulmonary monitoring are also performedon patients outside the hospital, such as patients who suffer fromasthma, high blood pressure, or cardiac arrhythmias. Cardiopulmonarymonitoring is also used during exercise performance tests of patientsand athletes. Clearly, monitoring and early detection of impendingcatastrophes are highly desirable in clinical medicine.

Continuous monitoring of cardiopulmonary wellbeing is presently focusedon physiological parameters that characterize, individually, theactivity of the heart and respiration. These parameters includemonitoring of the electrocardiogram, blood pressure in the arterial,venous and pulmonary circulations and cardiac output for assessing thecardiovascular system. The airway pressure, respiratory rate, tidalvolume, flow rate, pulse oximetry, exhaled CO₂, and esophageal pressuresare the respiratory parameters that are commonly monitored. Morerecently continuous overnight monitoring of breath sounds was introducedto detect and quantify wheezing activity in asthmatics. Additionalmethods were introduced to monitor patients inflicted with congestiveheart failure (CHF) to identify early decompensation. These methods arebased on continuous monitoring of the electrical impedance of thethorax, but have not generated reliable levels of sensitivity andspecificity. Monitoring of respiratory crackles has also been used as anearly sign of lung congestion.

The activities of the heart and the lungs are well known to be closelyinterrelated. The heart rate, blood pressure and blood flow into and outof the heart are influenced by the breathing cycle. The pressure insidethe chest becomes more negative during inspiration to enable inflow ofair into the lung alveoli. This sub-atmospheric pressure also affectsthe heart, blood vessels and blood flow. In particular, the increasedintrathoracic negative pressure expands the right atrium and ventricle,dilates and elongates the blood vessels, amplifies the ventricularfilling and changes the position of the inter ventricular septum. Inaddition, the changes in lung volume modify the afferent neuronalactivity in the vagus nerve, leading to modulation of the heart rateduring respiration. During quiet breathing the intrapleural pressuredecreases from about −3 mm Hg to about −6 mm Hg. This causes dilation ofthe intrathoracic segment of the vena cava, increased venous return tothe right atrium and ventricle. The increased diastolic filling of theright ventricle amplifies its stroke volume into the pulmonarycirculation by the well-known Starling mechanism. At the same time, theinspiratory displacement of the abdominal content by the contractingdiaphragm increases the intra abdominal pressure, which further pushesblood into the thoracic cavity. The intrathoracic pressure changesduring breathing also influence the output of the left ventricle, but toa lesser degree. During deep breathing, or in pulmonary diseases thataffect the mechanical properties of the airways and the lung parenchymathese, respiratory swings of flow, resistance and volume are greatlyexaggerated (Mountcastle V. O. Medical Physiology, 12^(th) ed. MosbyCompany, St. Louis 1968).

Positive pressure ventilation squeezes the pulmonary capillaries andincreases the resistance to blood flow through the lungs. Thesepressures may diminish the output of the right ventricle due toincreased afterload, while at the same time reducing the output of theleft ventricle due to fall in its diastolic filling and preload. Thesephenomena are known to result in wide fluctuations of the cardiac outputand blood pressure. Similar fluctuations are also seen during resistedbreathing, cough and isometric muscle straining.

Monitoring of heart sounds is well known to medicine and physiology formany years, even before the invention of the stethoscope by Laennec in1819. The first and second heart sounds are associated with closing ofthe atrioventricular and ventricular outlet valves, respectively. Theyare loud and distinct sounds with somewhat different amplitude andtemplate in different areas of precordial auscultation. The first andsecond heart sounds are modified, and sometimes are even completelymissing during diseases of the heart or the lungs. Additionally, the3^(rd) and 4^(th) heart sounds are well known to be associated withdefects of the left ventricular filling during diastole. Rumbling soundsheard in between the first and second sounds (systole) or the second andfirst sounds (diastole) are appropriately called systolic and diastolicmurmurs, respectively and are associated with abnormal blood flowthrough the narrowed or malfunctioning heart valves. Information onheart sounds, phonocardiography and the art of interpretation of heartsounds is readily available in many text books and articles, such as in“Rapid interpretation of heart sounds and murmurs” by Emanuel Stein,Abner J. Delman (Editors), Williams & Wilkins, 1997, 4th edition, thecontents of which are incorporated herein by reference.

The respiratory changes of cardiac activity are well known. Inparticular, the phenomenon of “Pulsus Paradoxus” is a recognized sign ofsevere asthma and airway narrowing. It is defined as a decline ofgreater than 12 mm Hg (in some texts 20 mm Hg) in the systolic bloodpressure during inspiration. Detection of Pulsus Paradoxus in a dyspneicpatient is an ominous sign that calls for aggressive and immediateintervention. On the other hand, cardiac arrhythmia, the accelerationand deceleration of the heart rate during the respiratory cycle is oftena benign and normal phenomenon, particularly in young children. The onlyavailable information on the effect of respiration on the heart soundsper-se is on the width of splitting of the second heart sound and oncertain cardiac murmurs. Otherwise, no information is available on thechanges in the heart sounds induced by respiration and on the extent ofthese changes during various cardiopulmonary conditions.

The failing heart condition is a cause for severe morbidity andmortality rates. This condition can be helped by a broad range of assistmethods. These include pharmaceutical agents (e.g., digitalis and otherpositive inotropic agents), mechanical support (e.g., intra aorticcounter pulsation, and intravascular coronary artery stent), and varioustypes of electrical stimulation (e.g., ventricular resynchronizationtherapy, Guidant's Contak CD, Medtronic's multi electrode epicardialpacing, Impulse Dynamics' timed refractory period current). All heartbetsynchronized methods must be monitored as regards their performance.This feedback can determine if, and to what extent the assistance iseffective. Such monitoring is either performed in intervals (e.g., byperiodic determination of the left ventricular ejection function), orcontinuous (e.g., by monitoring the left ventricular pressure—RemonMedical Technologies LTD of 7 Halamish Street, Caesaria Industrial Park,38900, Israel). To be effective, the mechanical and electrical assistmethods should be synchronized with the cardiac cycle so that they canbe activated at specific phases of the cardiac cycle. For example, inU.S. Pat. No. 6,285,906 the contents of which are incorporated herein byreference, impulse dynamics provides current stimuli at certain precisephases of the heart cycle. To do so, most methods use the EKG(electrocardiographic) signal to find the appropriate timing within thecardiac cycle for activation of the assistance. However, the EKG may notprovide sufficiently detailed or accurate information about the timingcardiac cycle, or may be contaminated by electromagnetic noise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. is a schematic description of the signal collection andprocessing device providing synchronized features of the cardiacactivity;

FIG. 2. is a schematic description of the entire process in accordancewith a preferred embodiment of the invention, performing propbabilisticanalysis.

FIG. 3. is a schematic description of the decision loop involvingheartbet synchronized control in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a novel method for monitoring theinterrelated functionality of the heart and the respiratory system bytracking the changes in cardiac sounds with respect to the activity ofthe heart during the respiratory cycle. A novelty of the new approach isthat the timing of each heart contraction within the breathing cycle isused as a cardinal parameter in the analysis of the heart soundsrelative to the electrocardiographic signal. By doing so, the heartsounds generated during similar segments of the respiratory cycle, forexample, the first part of inspiration, are analysed together togenerate respiratory synchronized stable templates and features of theheart sounds and their interaction with the EKG. Thus, the extent ofchanges in EKG—modulated heart sounds parameters during each phase ofthe respiratory cycle may be tracked over time to detect variability ofthe breathing related swings that indicate worsening of the status ofthe cardiopulmonary system. In addition, the invention facilitatestracking of the stable synchronized cardiac sounds features over timethat are indicative by themselves of the well-being of the mechanics ofthe heart and lungs. It is further disclosed that the determination ofthe stable synchronized cardiac sounds features is classified by thetiming and template of the EKG. Together, these new methods providevaluable information on the status and mechanics of the completecardiopulmonary system.

The invention overcomes two major hindrances to extraction of stablefeatures from heart sounds: the variability of the cardiac sounds duringthe respiratory cycle and changes in the sounds caused by variability ofthe electrical activity of the heart. The new method extracts featuressuch as amplitude, duration, frequency content, template and chirpcomponents of the heart sounds and clusters these features with respectto their timing in the respiratory cycle and/or their underlying EKGmorphology. This method preserves the attributes of the cardiac acousticsignal that are otherwise eliminated or greatly diminished by groupingthe sounds or their features without respect to their respiratory timingand EKG, as is currently learned by the existing art. The extraction ofsynchronized stable features greatly diminishes their stochasticvariability, so that any changes in the deterministic components of theheart sounds, such as those caused by changes in the mechanical activityof the heart, are more readily and accurately detected andcharacterized. The acoustic properties of the heart sounds reflect themechanics of the cardiac contraction, the integrity of the cardiacstructure and dynamics of flows, volumes and pressures in thecardiovascular system. Therefore, detection of changes in thesynchronised stable sounds relative to a baseline may be used to alertthe patient, his or her caregivers, or an automated algorithm or devicethat can be prompted to provide a remedy to the patient's condition.This new method, while preserving complete non-invasiveness, providesfar more comprehensive monitoring of the patient than the currentexisting methods such as the EKG.

The grouping of synchronised stable features of the heart sounds with orwithout respect to the EKG template defines a baseline extent of WithinBreath Variability (WBV). Any changes of the extent of variability ofthe sounds or their features within the respiratory cycle relative to aprevious period, such as the baseline, can be readily and accuratelydetected. These alterations of the WBV may be caused by increasedpressure swings within the chest due to altered breathing pattern orchanges in the resistance, compliance or other mechanical properties ofthe lung. Such changes are often seen in asthma patients, patientsinflicted by congestive heart failure (CHF), patients who areartificially ventilated, anaesthetised, or otherwise critically ill.

Analysis of the Heart Sounds

In FIG. 1 to which reference is now made the main stages in collectingand processing of the signals, in a preferred embodiment of theinvention are shown. Cardiac acoustic signals 20, respiratory acousticsignal 22 and EKG electric signal 24 are collected by the appropriatedetectors. The cardiac acoustic signal and the respiratory acousticsignal are separated and conditioned, by module 28, which includes in apreferred embodiment of the invention filtering, denoising,amplification, multi-sensor analysis for removal of background noise andrecording artifacts. Feature extraction module 30 of the inventionperforms segmentation. Heart sounds are segmented into S1, S2, S3, S4,the breathing activity is segmented into the Heart sounds are segmentedinto S1, S2, S3, S4, the breathing activity is segmented into fiverespiratory stages; first half of inspiration, second half ofinspiration, first half of active expiration, second half of activeexpiration and the post expiratory pause if present, and the EKG signalis segmented into its components; P, QRS, T waves. The segmented inputdata undergo timing analysis by module 32. Temporal segmentation featureparameters are extracted from the segmented data by module 34. Theparameters are estimated individually from heart sounds appearing at thedifferent respiratory stages. The EKG signal is also analyzed withrespect to the respiratory stages.

Heart sounds are acquired via a single microphone or a plurality ofmicrophones located on the chest, back and/or neck of the patient. Aftersignal conditioning that includes pre-amplification, the signal isband-pass filtered so that the respiratory component may be separatedfrom the heart sounds. The isolated sound structures are then detectedin the heart sounds signal and classified as S1, the first heart sound,and S2 the second heart sound. The degree of isolation (which is relatedto the beating rate) is estimated earlier from the data using a densityplot of the inter-beat duration. A temporal window, related to the timeinterval variability, is used to distinguish between S2 and S1. Furthervalidation of the S1/S2 segmentation may be provided by the EKG signalwhere the timing of S1 is known to correlate with the QRS sequence andthe timing of S2 corresponds to the T wave. After the different soundsare segmented, an attribute corresponding to the respective respiratorycycle is attached to each of them, so that they can be further collectedinto groups with their corresponding timing within the respiratorycycle. Averaging the different sounds and/or their attributes in thecorresponding respiratory cycle forms adaptive templates. Averaging isperformed within each timing interval on the raw signal, the frequencycontent representation, and on a time-frequency representation obtainedby short time Fourier transform, Wigner distribution analysis or a moreadapted time-frequency representation using continuous wavelet transformor best basis and discrete wavelet representation. The latter methodsinclude internal denoising (compression) which is achieved by removingthe low energy coefficients from the transformed signal representationbefore performing the inverse transform.

The time-frequency representation can be used for parameter estimationof the chirp properties (mainly S2). These properties include the chirpslope, namely the frequency modulation of the signal, start and stopfrequency as well as slope, and the amplitude changes in the signal.Properties related to the time-frequency representation are extractedusing conventional curve fitting techniques. Features related to theamplitude changes, e.g. increase in negative amplitude of S2, frequencycontent as is recorded by Fourier transform, and instantaneous frequencychanges which is recorded in the time-frequency representation. Theparameters corresponding to each of the above categories, and theirvariability may be empirically estimated from the data and compared to adata base or baseline values of these parameters. The variabilitytogether with the estimation of the mean, may further be used for theestimation of statistically significant changes in each of theparameters, both during a respiratory cycle and when compared with anormal baseline of the specific patient or of previously recorded andlabeled data.

The separation of the deterministic and stochastic signal components isdone during entropy-based denoising, e.g. with wavelet or best basisrepresentation. The wavelet coefficients with smallest variance, whichcorrespond to smallest contribution to the energy of the collection ofheart sounds, are set to zero. This parameter shrinkage removes the partof the signal that is independent of the heart sounds and can beconsidered noise.

A non exhaustive list of temporal segmentation parameters and features,with respect to the respiratory stages extracted from the heart sounds,respiratory cycle and EKG useful in the cardiac assessment of theinvention is provided next:

-   -   1. Amplitude and energy of S1 and S2 in each segment, (peak and        RMS) where S1 and S2 are the first and second heart sounds,        respectively.    -   2. Timing of S1 and S2 relative to the EKG and relative to each        other whereas the EKG represents the electrical activity of the        heart, measured with skin-surface electrodes.    -   3. Delta time between occurrence of S2 and S1 and occurrence of        S1 and the S2 of the previous heart cycle.    -   4. Delta time between the occurrence of S1 and occurrence of the        QRS wave.    -   5. Delta time between the occurrence of S2 and occurrence of the        P wave.    -   6. Delta time beween the occurrence of the P wave and the R        wave.    -   7. Frequency content and instantaneous frequency content of S1        and S2.    -   8. Cord length of S1 and S2.    -   9. Polarity of first and largest component of S1 and S2.    -   10. Amplitude incline and damping (decaying) rate constant of S1        and S2.    -   11. Chirp parameters of S1 and S2.    -   12. Duration of whole cardiac cycle (reciprocal of the heart        rate) as well as of each heart sound segment (i.e. of S1, S2,        S3, S4, or murmurs).    -   13. Signature (or shape) specific for each sensor by using        signal processing methods and/or polynomial fit. Differentiate        between deterministic elements (e.g. S1-4) and stochastic        elements such as murmurs and/or artefacts. Determine separately        for each frequency range. Obtain template pattern of the heart        sounds signature for each segment of the respiratory cycle.    -   14. Cross correlation between various precordial sites. An        expansion of this is creating a dynamic acoustic map of the        anterior chest using many sensors, each displayed on an array        with brightness indicating amplitude and color(s) indicating the        frequency content. The dynamics of the changing map can be        tracked for changes during the respiratory cycle.    -   15. Calculate the deconvolution of the heart        sounds/ballistographic signal in order to estimate the        mechanical activity at the source that generates the signs.    -   16. An AM/FM Modulation decomposition of the heart sound to        analyse independently amplitude changes and frequency changes in        the heart sounds.    -   17. Amplitude multiplied by the width of each heart sound which        is an approximation to the temporal length of the heart sounds        times the peak amplitude of each heart sound. Energy of some        components of the heart sound as is measured in a time/frequency        representation which can be adapted to the signals.

Using the above parameters and features, deterministic and stochasticcomponents of the features and parameters are extracted. This definesthe acceptable boundaries for each feature and parameter. Suchboundaries are determined by the extent to which the parameters varywhilst the patient is in stable condition. In a preferred embodiment,these boundaries are defined by the mean and n×SD of each parameter andfeature for each the respiratory stages, where n is 2 for example. Inparticular, the (potentially non-linear) boundaries of thehyper-parameter space of the full set of the parameters are determined.Thus, for example, it is possible that a patient can be at a point whereeach of the parameters and features is within the defined boundaries,but the full set of parameters is outside of the boundaries.

A probabilistic model, which is estimated in a preferred embodiment, viacalculation of maximum likelihood, is applied to the processed data tofurther refine the model estimation. This binds together the temporalprogress from one respiratory stage to another (via a hidden Markovmodel). At this stage, additional information from other sources such asblood pressure, oxygen saturation, patient weight, chest electricalimpedance and other means, is fused. The full temporal model and set ofsegmented features are then analyzed to determine scoring of deviationfrom normality, or from the acceptable patients' boundaries.

The probabilistic analysis assumes a Gaussian distribution to thecontinuous features of the model and a binomial (sometimes multinomial)distribution to the categorical parameters. Following the estimation ofthe mean and standard deviation, significance or abnormality of anobserved features may be determined and using a probability model whichtakes into account the probability of a collection of features (takenfrom an already collected and soon to be collected data), one canestimate the probability of various abnormal events.

In FIG. 2 to which reference is now made, the whole sequence of stepsfrom the signal transduction to model display is describedschematically. In step 50 signals are transduced to produce acorreponding electric current. In step 52 the signals are conditioned.In step 54 the signals are segmented to express the segments of thephysiological rhytmicity. In step 56 Parameters are extracted, and instep 58 the deterministic components are separated from the stochasticcomponents. In step 60 the probabilistic analysis is performed,accepting physiological data at step 62, and in step 64 scoring anddisplay are provided. Although not shown, the probabilistic analysisperformed reflects on the signal segmentation to influence thesegmentation process.

Sensors for Collecting Signals of the Heart and Respiratory System

In general, apart from the ubiquitous EKG which senses electricalsignals of the body, all other sensors taking part in the signalcollection in accordance with the present invention are acoustic ormechano-acoustic sensors. Such sensors transduce mechanical pressure, oracoustic energy to electrical energy further to be analyzedelectronically. Acoustic sensors for sensing activity of the heart todetermine cardiac mechanical performance, which include typicallyrhythmicity, fine cardiac temporal cycle variability and musclecontractility, can be patched on the patient's chest or can be implantedsubcutaneously. Conditioning respiratory activity sounds and respiratoryactivity mainly for the determination of respiratory rhythm ispreferably performed using impedance plethysmography. Several sensorscan be used concomitantly for obtaining data about the sounds.

Electronic Hardware for Processing the Data and Providing DiagnosticAids and Control Signals

Transduced electric signals from the sensors, as well as EKG signals,are collected by data collecting hardware, sampled and digitized on adedicated processor or in a computer. All processing and statisticalcalculations and model assessment are done in the computerizedenvironment. A GUI or a printer is typically used to produce a report tothe medical team in charge. In that case that control signals arerequired, this can be supplied by a controller linked to the processoror computer wherein the processing is performed. If more than one sensoris used for obtaining sounds data, a procedure for correlation betweenthe sensors may be implemented. The implementation of such a procedureis typically carried out for reducing noise.

Medical Uses of the Invention

A simple example of the method of the invention includes segmenting thebreathing cycle into exhaling and inhaling segments, and recordingcontracting heart sound at these inhaling and the exhaling segments.Sound amplitude samples are collected and then averaged, average A forthe set of samples representing the exhaling segment period and averageB for the set of samples representing the inhaling segment period.Statistical difference is calculated between the averages A and B,providing evidence regarding quantitative difference in magnitudebetween the two sets. The physiological significance of the differenceis such that the larger difference is indicative of a pronouncedtendency of the heart to be limited by the expanding lungs.

These parameters and changes may be used to detect early changesassociated with pulmonary and/or hemodynamic alterations. These includeCHF (changes in the heart's mechanical activity, the lungs specificgravity (density) and increased fluctuations of intrathoracic pressure);mechanical ventilation (changes in PEEP, airway resistance), changes inhydration and blood volume (shock) (cardiac mechanical activity,increased breathing activity), hypertension (increased cardiacmechanical activity), myocardial infarction (changes in cardiaccontraction sequence).

The detection of each of the above-listed conditions is done bycomparing the respiratory synchronized cardiac sounds or featuresthereof to a patient-specific baseline, or to a template consisting ofdata from an aggregate of normal and abnormal patients. A system of theinvention or any system using the method of the invention are eithertherapeutic or diagnostic.

Cardiac Synchronization Methodology and Usage in Diagnostic andTherapeutic Devices

Assessment of the mechanical condition of the heart performed inaccordance with the invention can be used to help treating the failingheart. An acoustic heartbeat synchronized method comprises, inaccordance with the present invention, one or more sensors of heartsounds that can pick up and transduce the heart sounds of a patient intoelectrical signal and means for amplifying the signal, typically but notnecessarily a sensor for breathing activity and means for synchronizingthe signals. The heart sounds timing signal can be used to trigger theactivity of a heartbeat synchronized device, either directly or with adelay. Such delay may be pre-determined or modified automatically, basedon information on the heart sounds amplitude, the interval between them,or their amplitude and frequency content.

In FIG. 3, to which reference is now made, an automated controlmechanism a heartbeat synchronized device using a synchronizationalgorithm of the present invention, is shown. Acoustic cardiac signalsare transduced 80 and respiratory signals 82 are transduced to becollected by the monitor 84. Subsequently, they are processed at step 86by the method of the invention to extract accurate parameters of theheart. If the heart is regarded failing at diagnostic step 88, theheartbeat synchronized device is activated.

The heart sounds timing signal is used to actuate a demand-type orsynchronized cardiac pacemaker. This pacemaker is chronically implanted,or temporarily used to defibrillate a fibrillating heart, such as usedin automatic or manually triggered electric defibrillators. Severaltherapeutic and diagnostic systems can benefit from using the heartbeatsynchronization concept embodied in the invention. Such systems areintra-aortic balloon pump, left ventricular cardiac assist device,coronary angiography diagnostic devices, cardiac imaging devices suchas, but not limited to, CT, MRI and SPECT diagnostic technologies.

Monitoring Variability Changes

The grouping of synchronized stable features of the heart sounds with orwithout respect to the EKG template defines a baseline extent of withinbreath variability (WBV). Any changes of the extent of variability ofthe sounds or their features within the respiratory cycle relative to aprevious period, such as the baseline, can be readily and accuratelydetected. These alterations of the WBV may be caused by increasedpressure swings within the chest due to altered breathing pattern orchanges in the resistance, compliance or other mechanical properties ofthe lung. Such changes are often seen in asthma patients, patientsinflicted by congestive heart failure (CHF), patients who areartificially ventilated, anesthetized, or otherwise critically ill. Whensuch changes are found they can be used to alert the patient, his or hercaregivers, or an automated algorithm or device that can be prompted toprovide a remedy to the patient's condition.

Short Duration Analysis

Known in the art are changes in the duration of each heartbeat inassociation with the breathing cycle. These changes are usually detectedby measuring the distance between consecutive “R” waves in the EKG thatare called “the R-R interval”. The changes in the duration of the R-Rinterval over time are referred to as heart rRate variability” (HRV).HRV is considered as indicative of the activity of the sympathetic andparasympathetic control of the heart. It is substantially reduced ingravely ill patients. Recently, changes (reduction) in HRV have beenassociated with particulate matter (PM) air pollution and are believedto be an important mechanism in the increased mortality due to PMpollution. Other conditions, such as the sleep apnea syndrome, chroniccardiac ailments and the use of certain medications are also known to beassociated with changes in HRV.

In accordance with the present invention, analysis of the short-termduration changes of systolic time (Tsys) and diastolic time (Tdias) isperformed and studied with the respiratory cycle as well as the otheramplitude and signal morphology features that have been discussed above.Of particular interest are correlation between short term changes whichare indicative of a well-heart being such as increase in Tsys whichleads to a an increase in the amplitude and sharpness of S2, indicatingan increase in duration that is due to more blood flow into the heart,or a negative correlation between Tsys and Tdias, indicating is moreelaborated breathing (possible air-way obstruction) but healthy heartdynamics, vs. changes in Tsys that are not correlated with Tdias andvice versa.

The method of the invention can be used to differentiate betweeninhalation and exhalation phases of the respiratory cycle. It can alsobe used to determine the template and variations of S3, S4 and cardiacmurmurs.

Use of the Invention in Irregular Breathing Instances

The method of the invention can be applied in the course of irregularbreathing. Forced breathing interruptions are examples in which themethod of the invention may be used to analyze the condition of theheart. More particular examples for such irregularities are breath holdafter deep respiration and Valsalva maneuver while agonal respiration isa pathological instance of interrupted respiration.

Use of the Method of the Invention in Diagnosing Extra Cardiac BloodVessels

The method of the invention can be applied to improve the diagnosis ofextra-cardiac blood vessels using MRI (magnetic resonance imaging) aswell as other imaging methods. The application with MRI is known in theart as MRA (magnetic resonance angiography), and includes applying amagnetic field to the region of interest, and mapping the effect of themagnetic field on various constituents of the region of interest. Thetechnique is very useful, but the lengthy data acquisition time of theprocedure causes blurring of the image is obtained. In order to improvethe image, synchronizing the MR imaging with the heartbeat and otherimaging methods or with the heartbeat as synchronized versus respiratoryactivity improves the clarity of the image.

1. A method for analysing the functionalities of the heart and of therespiratory system of a patient, comprising: segmenting cyclic heartbeating sounds into physically defined classes and independentlysegmenting cyclic breathing cycle into physiologically defined classes;associating segments of same class of said heart sounds with segments ofsame class of said breathing sounds, and correlating physicalcharacteristics of said heart sounds of same class with physicalcharacteristics of said breathing sounds of same class.
 2. A method foranalyzing the functionality of the heart and the respiratory system asin claim 1, and wherein said cyclic heart beating sounds aresynchronized by features of an EKG.
 3. A method for analysing a changein the functionality of the heart and the respiratory system of apatient, comprising: identifying the respiratory activity and cardiacsounds; segmenting said respiratory and said cardiac sounds; classifyingsaid segments of said respiratory and said cardiac sounds; extractingfeatures of said classes; comparing the features of said classes, anddetermining the significance of the deviation of a set of said featuresfrom a respective set of baseline values.
 4. A method for synchronizinga heartbeat synchronized system comprising: segmenting said respiratoryactivity and said cardiac sounds, wherein data of cardiac sounds isobtained from at least one heart sound sensor; correlating physicalcharacteristics of said heart sounds of same class with physicalcharacteristics of said breathing sounds of same class; determining thetemporal signal structure of the heart, and sending control signal tothe heartbeat synchronized system.
 5. A method for synchronizing aheartbeat synchronized system as in claim 4, comprising: segmenting saidcardiac sounds data obtained from a plurality of heart sound sensorsrespectively; correlating physical characteristics of said heart soundsof same class using data of each sensor respectively with physicalcharacteristics of said breathing sounds of same class r; determiningthe temporal signal structure of the heart, sending control signal tothe heartbeat synchronized system.
 6. A diagnostic method forsynchronizing a heartbeat synchronized system as in claim
 4. 7. Atherapeutic method for synchronizing a heartbeat synchronized system asin claim
 4. 8. A system for monitoring the interrelated functionality ofthe heart and the respiratory system, comprising: at least one means forcollecting heart beating sounds; means for collecting cyclic sound ofthe respiratory system, and a means for processing said sounds.
 9. Asystem for monitoring the interrelated functionality of the heart andthe respiratory system as in claim 8 and wherein all sounds arecollected by a plurality of means.
 10. A system for monitoring theinterrelated functionality of the heart and the respiratory system as inclaim 8 and wherein said system is a part of a heartbeat synchronizeddevice.
 11. A system as in claim 10 wherein said heartbeat synchronizedsystem is a monitoring device.
 12. A system as in claim 10 wherein saidheartbeat synchronized system is an intra-aortic balloon pump.
 13. Asystem as in claim 10 wherein said heartbeat synchronized system is aleft ventricular cardiac assist device.
 14. A system as in claim 10wherein said heartbeat synchronized system is a CT coronary angiographydiagnostic device.
 15. A system as in claim 10 wherein said heartbeatsynchronized system is a SPECT diagnostic device.
 16. A method forimproving magnetic resonance angiography wherein said magnetic resonanceangiography acquisition time is synchronized with the synchronizedheartbeat.